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<FONT color="green">001</FONT>    /*<a name="line.1"></a>
<FONT color="green">002</FONT>     * Licensed to the Apache Software Foundation (ASF) under one or more<a name="line.2"></a>
<FONT color="green">003</FONT>     * contributor license agreements.  See the NOTICE file distributed with<a name="line.3"></a>
<FONT color="green">004</FONT>     * this work for additional information regarding copyright ownership.<a name="line.4"></a>
<FONT color="green">005</FONT>     * The ASF licenses this file to You under the Apache License, Version 2.0<a name="line.5"></a>
<FONT color="green">006</FONT>     * (the "License"); you may not use this file except in compliance with<a name="line.6"></a>
<FONT color="green">007</FONT>     * the License.  You may obtain a copy of the License at<a name="line.7"></a>
<FONT color="green">008</FONT>     *<a name="line.8"></a>
<FONT color="green">009</FONT>     *      http://www.apache.org/licenses/LICENSE-2.0<a name="line.9"></a>
<FONT color="green">010</FONT>     *<a name="line.10"></a>
<FONT color="green">011</FONT>     * Unless required by applicable law or agreed to in writing, software<a name="line.11"></a>
<FONT color="green">012</FONT>     * distributed under the License is distributed on an "AS IS" BASIS,<a name="line.12"></a>
<FONT color="green">013</FONT>     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.<a name="line.13"></a>
<FONT color="green">014</FONT>     * See the License for the specific language governing permissions and<a name="line.14"></a>
<FONT color="green">015</FONT>     * limitations under the License.<a name="line.15"></a>
<FONT color="green">016</FONT>     */<a name="line.16"></a>
<FONT color="green">017</FONT>    package org.apache.commons.math3.stat.regression;<a name="line.17"></a>
<FONT color="green">018</FONT>    <a name="line.18"></a>
<FONT color="green">019</FONT>    import java.io.Serializable;<a name="line.19"></a>
<FONT color="green">020</FONT>    import java.util.Arrays;<a name="line.20"></a>
<FONT color="green">021</FONT>    import org.apache.commons.math3.util.FastMath;<a name="line.21"></a>
<FONT color="green">022</FONT>    import org.apache.commons.math3.util.MathArrays;<a name="line.22"></a>
<FONT color="green">023</FONT>    import org.apache.commons.math3.exception.OutOfRangeException;<a name="line.23"></a>
<FONT color="green">024</FONT>    <a name="line.24"></a>
<FONT color="green">025</FONT>    /**<a name="line.25"></a>
<FONT color="green">026</FONT>     * Results of a Multiple Linear Regression model fit.<a name="line.26"></a>
<FONT color="green">027</FONT>     *<a name="line.27"></a>
<FONT color="green">028</FONT>     * @version $Id: RegressionResults.java 1392342 2012-10-01 14:08:52Z psteitz $<a name="line.28"></a>
<FONT color="green">029</FONT>     * @since 3.0<a name="line.29"></a>
<FONT color="green">030</FONT>     */<a name="line.30"></a>
<FONT color="green">031</FONT>    public class RegressionResults implements Serializable {<a name="line.31"></a>
<FONT color="green">032</FONT>    <a name="line.32"></a>
<FONT color="green">033</FONT>        /** INDEX of Sum of Squared Errors */<a name="line.33"></a>
<FONT color="green">034</FONT>        private static final int SSE_IDX = 0;<a name="line.34"></a>
<FONT color="green">035</FONT>        /** INDEX of Sum of Squares of Model */<a name="line.35"></a>
<FONT color="green">036</FONT>        private static final int SST_IDX = 1;<a name="line.36"></a>
<FONT color="green">037</FONT>        /** INDEX of R-Squared of regression */<a name="line.37"></a>
<FONT color="green">038</FONT>        private static final int RSQ_IDX = 2;<a name="line.38"></a>
<FONT color="green">039</FONT>        /** INDEX of Mean Squared Error */<a name="line.39"></a>
<FONT color="green">040</FONT>        private static final int MSE_IDX = 3;<a name="line.40"></a>
<FONT color="green">041</FONT>        /** INDEX of Adjusted R Squared */<a name="line.41"></a>
<FONT color="green">042</FONT>        private static final int ADJRSQ_IDX = 4;<a name="line.42"></a>
<FONT color="green">043</FONT>        /** UID */<a name="line.43"></a>
<FONT color="green">044</FONT>        private static final long serialVersionUID = 1l;<a name="line.44"></a>
<FONT color="green">045</FONT>        /** regression slope parameters */<a name="line.45"></a>
<FONT color="green">046</FONT>        private final double[] parameters;<a name="line.46"></a>
<FONT color="green">047</FONT>        /** variance covariance matrix of parameters */<a name="line.47"></a>
<FONT color="green">048</FONT>        private final double[][] varCovData;<a name="line.48"></a>
<FONT color="green">049</FONT>        /** boolean flag for variance covariance matrix in symm compressed storage */<a name="line.49"></a>
<FONT color="green">050</FONT>        private final boolean isSymmetricVCD;<a name="line.50"></a>
<FONT color="green">051</FONT>        /** rank of the solution */<a name="line.51"></a>
<FONT color="green">052</FONT>        @SuppressWarnings("unused")<a name="line.52"></a>
<FONT color="green">053</FONT>        private final int rank;<a name="line.53"></a>
<FONT color="green">054</FONT>        /** number of observations on which results are based */<a name="line.54"></a>
<FONT color="green">055</FONT>        private final long nobs;<a name="line.55"></a>
<FONT color="green">056</FONT>        /** boolean flag indicator of whether a constant was included*/<a name="line.56"></a>
<FONT color="green">057</FONT>        private final boolean containsConstant;<a name="line.57"></a>
<FONT color="green">058</FONT>        /** array storing global results, SSE, MSE, RSQ, adjRSQ */<a name="line.58"></a>
<FONT color="green">059</FONT>        private final double[] globalFitInfo;<a name="line.59"></a>
<FONT color="green">060</FONT>    <a name="line.60"></a>
<FONT color="green">061</FONT>        /**<a name="line.61"></a>
<FONT color="green">062</FONT>         *  Set the default constructor to private access<a name="line.62"></a>
<FONT color="green">063</FONT>         *  to prevent inadvertent instantiation<a name="line.63"></a>
<FONT color="green">064</FONT>         */<a name="line.64"></a>
<FONT color="green">065</FONT>        @SuppressWarnings("unused")<a name="line.65"></a>
<FONT color="green">066</FONT>        private RegressionResults() {<a name="line.66"></a>
<FONT color="green">067</FONT>            this.parameters = null;<a name="line.67"></a>
<FONT color="green">068</FONT>            this.varCovData = null;<a name="line.68"></a>
<FONT color="green">069</FONT>            this.rank = -1;<a name="line.69"></a>
<FONT color="green">070</FONT>            this.nobs = -1;<a name="line.70"></a>
<FONT color="green">071</FONT>            this.containsConstant = false;<a name="line.71"></a>
<FONT color="green">072</FONT>            this.isSymmetricVCD = false;<a name="line.72"></a>
<FONT color="green">073</FONT>            this.globalFitInfo = null;<a name="line.73"></a>
<FONT color="green">074</FONT>        }<a name="line.74"></a>
<FONT color="green">075</FONT>    <a name="line.75"></a>
<FONT color="green">076</FONT>        /**<a name="line.76"></a>
<FONT color="green">077</FONT>         * Constructor for Regression Results.<a name="line.77"></a>
<FONT color="green">078</FONT>         *<a name="line.78"></a>
<FONT color="green">079</FONT>         * @param parameters a double array with the regression slope estimates<a name="line.79"></a>
<FONT color="green">080</FONT>         * @param varcov the variance covariance matrix, stored either in a square matrix<a name="line.80"></a>
<FONT color="green">081</FONT>         * or as a compressed<a name="line.81"></a>
<FONT color="green">082</FONT>         * @param isSymmetricCompressed a flag which denotes that the variance covariance<a name="line.82"></a>
<FONT color="green">083</FONT>         * matrix is in symmetric compressed format<a name="line.83"></a>
<FONT color="green">084</FONT>         * @param nobs the number of observations of the regression estimation<a name="line.84"></a>
<FONT color="green">085</FONT>         * @param rank the number of independent variables in the regression<a name="line.85"></a>
<FONT color="green">086</FONT>         * @param sumy the sum of the independent variable<a name="line.86"></a>
<FONT color="green">087</FONT>         * @param sumysq the sum of the squared independent variable<a name="line.87"></a>
<FONT color="green">088</FONT>         * @param sse sum of squared errors<a name="line.88"></a>
<FONT color="green">089</FONT>         * @param containsConstant true model has constant,  false model does not have constant<a name="line.89"></a>
<FONT color="green">090</FONT>         * @param copyData if true a deep copy of all input data is made, if false only references<a name="line.90"></a>
<FONT color="green">091</FONT>         * are copied and the RegressionResults become mutable<a name="line.91"></a>
<FONT color="green">092</FONT>         */<a name="line.92"></a>
<FONT color="green">093</FONT>        public RegressionResults(<a name="line.93"></a>
<FONT color="green">094</FONT>                final double[] parameters, final double[][] varcov,<a name="line.94"></a>
<FONT color="green">095</FONT>                final boolean isSymmetricCompressed,<a name="line.95"></a>
<FONT color="green">096</FONT>                final long nobs, final int rank,<a name="line.96"></a>
<FONT color="green">097</FONT>                final double sumy, final double sumysq, final double sse,<a name="line.97"></a>
<FONT color="green">098</FONT>                final boolean containsConstant,<a name="line.98"></a>
<FONT color="green">099</FONT>                final boolean copyData) {<a name="line.99"></a>
<FONT color="green">100</FONT>            if (copyData) {<a name="line.100"></a>
<FONT color="green">101</FONT>                this.parameters = MathArrays.copyOf(parameters);<a name="line.101"></a>
<FONT color="green">102</FONT>                this.varCovData = new double[varcov.length][];<a name="line.102"></a>
<FONT color="green">103</FONT>                for (int i = 0; i &lt; varcov.length; i++) {<a name="line.103"></a>
<FONT color="green">104</FONT>                    this.varCovData[i] = MathArrays.copyOf(varcov[i]);<a name="line.104"></a>
<FONT color="green">105</FONT>                }<a name="line.105"></a>
<FONT color="green">106</FONT>            } else {<a name="line.106"></a>
<FONT color="green">107</FONT>                this.parameters = parameters;<a name="line.107"></a>
<FONT color="green">108</FONT>                this.varCovData = varcov;<a name="line.108"></a>
<FONT color="green">109</FONT>            }<a name="line.109"></a>
<FONT color="green">110</FONT>            this.isSymmetricVCD = isSymmetricCompressed;<a name="line.110"></a>
<FONT color="green">111</FONT>            this.nobs = nobs;<a name="line.111"></a>
<FONT color="green">112</FONT>            this.rank = rank;<a name="line.112"></a>
<FONT color="green">113</FONT>            this.containsConstant = containsConstant;<a name="line.113"></a>
<FONT color="green">114</FONT>            this.globalFitInfo = new double[5];<a name="line.114"></a>
<FONT color="green">115</FONT>            Arrays.fill(this.globalFitInfo, Double.NaN);<a name="line.115"></a>
<FONT color="green">116</FONT>    <a name="line.116"></a>
<FONT color="green">117</FONT>            if (rank &gt; 0) {<a name="line.117"></a>
<FONT color="green">118</FONT>                this.globalFitInfo[SST_IDX] = containsConstant ?<a name="line.118"></a>
<FONT color="green">119</FONT>                        (sumysq - sumy * sumy / nobs) : sumysq;<a name="line.119"></a>
<FONT color="green">120</FONT>            }<a name="line.120"></a>
<FONT color="green">121</FONT>    <a name="line.121"></a>
<FONT color="green">122</FONT>            this.globalFitInfo[SSE_IDX] = sse;<a name="line.122"></a>
<FONT color="green">123</FONT>            this.globalFitInfo[MSE_IDX] = this.globalFitInfo[SSE_IDX] /<a name="line.123"></a>
<FONT color="green">124</FONT>                    (nobs - rank);<a name="line.124"></a>
<FONT color="green">125</FONT>            this.globalFitInfo[RSQ_IDX] = 1.0 -<a name="line.125"></a>
<FONT color="green">126</FONT>                    this.globalFitInfo[SSE_IDX] /<a name="line.126"></a>
<FONT color="green">127</FONT>                    this.globalFitInfo[SST_IDX];<a name="line.127"></a>
<FONT color="green">128</FONT>    <a name="line.128"></a>
<FONT color="green">129</FONT>            if (!containsConstant) {<a name="line.129"></a>
<FONT color="green">130</FONT>                this.globalFitInfo[ADJRSQ_IDX] = 1.0-<a name="line.130"></a>
<FONT color="green">131</FONT>                        (1.0 - this.globalFitInfo[RSQ_IDX]) *<a name="line.131"></a>
<FONT color="green">132</FONT>                        ( (double) nobs / ( (double) (nobs - rank)));<a name="line.132"></a>
<FONT color="green">133</FONT>            } else {<a name="line.133"></a>
<FONT color="green">134</FONT>                this.globalFitInfo[ADJRSQ_IDX] = 1.0 - (sse * (nobs - 1.0)) /<a name="line.134"></a>
<FONT color="green">135</FONT>                        (globalFitInfo[SST_IDX] * (nobs - rank));<a name="line.135"></a>
<FONT color="green">136</FONT>            }<a name="line.136"></a>
<FONT color="green">137</FONT>        }<a name="line.137"></a>
<FONT color="green">138</FONT>    <a name="line.138"></a>
<FONT color="green">139</FONT>        /**<a name="line.139"></a>
<FONT color="green">140</FONT>         * &lt;p&gt;Returns the parameter estimate for the regressor at the given index.&lt;/p&gt;<a name="line.140"></a>
<FONT color="green">141</FONT>         *<a name="line.141"></a>
<FONT color="green">142</FONT>         * &lt;p&gt;A redundant regressor will have its redundancy flag set, as well as<a name="line.142"></a>
<FONT color="green">143</FONT>         *  a parameters estimated equal to {@code Double.NaN}&lt;/p&gt;<a name="line.143"></a>
<FONT color="green">144</FONT>         *<a name="line.144"></a>
<FONT color="green">145</FONT>         * @param index Index.<a name="line.145"></a>
<FONT color="green">146</FONT>         * @return the parameters estimated for regressor at index.<a name="line.146"></a>
<FONT color="green">147</FONT>         * @throws OutOfRangeException if {@code index} is not in the interval<a name="line.147"></a>
<FONT color="green">148</FONT>         * {@code [0, number of parameters)}.<a name="line.148"></a>
<FONT color="green">149</FONT>         */<a name="line.149"></a>
<FONT color="green">150</FONT>        public double getParameterEstimate(int index) throws OutOfRangeException {<a name="line.150"></a>
<FONT color="green">151</FONT>            if (parameters == null) {<a name="line.151"></a>
<FONT color="green">152</FONT>                return Double.NaN;<a name="line.152"></a>
<FONT color="green">153</FONT>            }<a name="line.153"></a>
<FONT color="green">154</FONT>            if (index &lt; 0 || index &gt;= this.parameters.length) {<a name="line.154"></a>
<FONT color="green">155</FONT>                throw new OutOfRangeException(index, 0, this.parameters.length - 1);<a name="line.155"></a>
<FONT color="green">156</FONT>            }<a name="line.156"></a>
<FONT color="green">157</FONT>            return this.parameters[index];<a name="line.157"></a>
<FONT color="green">158</FONT>        }<a name="line.158"></a>
<FONT color="green">159</FONT>    <a name="line.159"></a>
<FONT color="green">160</FONT>        /**<a name="line.160"></a>
<FONT color="green">161</FONT>         * &lt;p&gt;Returns a copy of the regression parameters estimates.&lt;/p&gt;<a name="line.161"></a>
<FONT color="green">162</FONT>         *<a name="line.162"></a>
<FONT color="green">163</FONT>         * &lt;p&gt;The parameter estimates are returned in the natural order of the data.&lt;/p&gt;<a name="line.163"></a>
<FONT color="green">164</FONT>         *<a name="line.164"></a>
<FONT color="green">165</FONT>         * &lt;p&gt;A redundant regressor will have its redundancy flag set, as will<a name="line.165"></a>
<FONT color="green">166</FONT>         *  a parameter estimate equal to {@code Double.NaN}.&lt;/p&gt;<a name="line.166"></a>
<FONT color="green">167</FONT>         *<a name="line.167"></a>
<FONT color="green">168</FONT>         * @return array of parameter estimates, null if no estimation occurred<a name="line.168"></a>
<FONT color="green">169</FONT>         */<a name="line.169"></a>
<FONT color="green">170</FONT>        public double[] getParameterEstimates() {<a name="line.170"></a>
<FONT color="green">171</FONT>            if (this.parameters == null) {<a name="line.171"></a>
<FONT color="green">172</FONT>                return null;<a name="line.172"></a>
<FONT color="green">173</FONT>            }<a name="line.173"></a>
<FONT color="green">174</FONT>            return MathArrays.copyOf(parameters);<a name="line.174"></a>
<FONT color="green">175</FONT>        }<a name="line.175"></a>
<FONT color="green">176</FONT>    <a name="line.176"></a>
<FONT color="green">177</FONT>        /**<a name="line.177"></a>
<FONT color="green">178</FONT>         * Returns the &lt;a href="http://www.xycoon.com/standerrorb(1).htm"&gt;standard<a name="line.178"></a>
<FONT color="green">179</FONT>         * error of the parameter estimate at index&lt;/a&gt;,<a name="line.179"></a>
<FONT color="green">180</FONT>         * usually denoted s(b&lt;sub&gt;index&lt;/sub&gt;).<a name="line.180"></a>
<FONT color="green">181</FONT>         *<a name="line.181"></a>
<FONT color="green">182</FONT>         * @param index Index.<a name="line.182"></a>
<FONT color="green">183</FONT>         * @return the standard errors associated with parameters estimated at index.<a name="line.183"></a>
<FONT color="green">184</FONT>         * @throws OutOfRangeException if {@code index} is not in the interval<a name="line.184"></a>
<FONT color="green">185</FONT>         * {@code [0, number of parameters)}.<a name="line.185"></a>
<FONT color="green">186</FONT>         */<a name="line.186"></a>
<FONT color="green">187</FONT>        public double getStdErrorOfEstimate(int index) throws OutOfRangeException {<a name="line.187"></a>
<FONT color="green">188</FONT>            if (parameters == null) {<a name="line.188"></a>
<FONT color="green">189</FONT>                return Double.NaN;<a name="line.189"></a>
<FONT color="green">190</FONT>            }<a name="line.190"></a>
<FONT color="green">191</FONT>            if (index &lt; 0 || index &gt;= this.parameters.length) {<a name="line.191"></a>
<FONT color="green">192</FONT>                throw new OutOfRangeException(index, 0, this.parameters.length - 1);<a name="line.192"></a>
<FONT color="green">193</FONT>            }<a name="line.193"></a>
<FONT color="green">194</FONT>            double var = this.getVcvElement(index, index);<a name="line.194"></a>
<FONT color="green">195</FONT>            if (!Double.isNaN(var) &amp;&amp; var &gt; Double.MIN_VALUE) {<a name="line.195"></a>
<FONT color="green">196</FONT>                return FastMath.sqrt(var);<a name="line.196"></a>
<FONT color="green">197</FONT>            }<a name="line.197"></a>
<FONT color="green">198</FONT>            return Double.NaN;<a name="line.198"></a>
<FONT color="green">199</FONT>        }<a name="line.199"></a>
<FONT color="green">200</FONT>    <a name="line.200"></a>
<FONT color="green">201</FONT>        /**<a name="line.201"></a>
<FONT color="green">202</FONT>         * &lt;p&gt;Returns the &lt;a href="http://www.xycoon.com/standerrorb(1).htm"&gt;standard<a name="line.202"></a>
<FONT color="green">203</FONT>         * error of the parameter estimates&lt;/a&gt;,<a name="line.203"></a>
<FONT color="green">204</FONT>         * usually denoted s(b&lt;sub&gt;i&lt;/sub&gt;).&lt;/p&gt;<a name="line.204"></a>
<FONT color="green">205</FONT>         *<a name="line.205"></a>
<FONT color="green">206</FONT>         * &lt;p&gt;If there are problems with an ill conditioned design matrix then the regressor<a name="line.206"></a>
<FONT color="green">207</FONT>         * which is redundant will be assigned &lt;code&gt;Double.NaN&lt;/code&gt;. &lt;/p&gt;<a name="line.207"></a>
<FONT color="green">208</FONT>         *<a name="line.208"></a>
<FONT color="green">209</FONT>         * @return an array standard errors associated with parameters estimates,<a name="line.209"></a>
<FONT color="green">210</FONT>         *  null if no estimation occurred<a name="line.210"></a>
<FONT color="green">211</FONT>         */<a name="line.211"></a>
<FONT color="green">212</FONT>        public double[] getStdErrorOfEstimates() {<a name="line.212"></a>
<FONT color="green">213</FONT>            if (parameters == null) {<a name="line.213"></a>
<FONT color="green">214</FONT>                return null;<a name="line.214"></a>
<FONT color="green">215</FONT>            }<a name="line.215"></a>
<FONT color="green">216</FONT>            double[] se = new double[this.parameters.length];<a name="line.216"></a>
<FONT color="green">217</FONT>            for (int i = 0; i &lt; this.parameters.length; i++) {<a name="line.217"></a>
<FONT color="green">218</FONT>                double var = this.getVcvElement(i, i);<a name="line.218"></a>
<FONT color="green">219</FONT>                if (!Double.isNaN(var) &amp;&amp; var &gt; Double.MIN_VALUE) {<a name="line.219"></a>
<FONT color="green">220</FONT>                    se[i] = FastMath.sqrt(var);<a name="line.220"></a>
<FONT color="green">221</FONT>                    continue;<a name="line.221"></a>
<FONT color="green">222</FONT>                }<a name="line.222"></a>
<FONT color="green">223</FONT>                se[i] = Double.NaN;<a name="line.223"></a>
<FONT color="green">224</FONT>            }<a name="line.224"></a>
<FONT color="green">225</FONT>            return se;<a name="line.225"></a>
<FONT color="green">226</FONT>        }<a name="line.226"></a>
<FONT color="green">227</FONT>    <a name="line.227"></a>
<FONT color="green">228</FONT>        /**<a name="line.228"></a>
<FONT color="green">229</FONT>         * &lt;p&gt;Returns the covariance between regression parameters i and j.&lt;/p&gt;<a name="line.229"></a>
<FONT color="green">230</FONT>         *<a name="line.230"></a>
<FONT color="green">231</FONT>         * &lt;p&gt;If there are problems with an ill conditioned design matrix then the covariance<a name="line.231"></a>
<FONT color="green">232</FONT>         * which involves redundant columns will be assigned {@code Double.NaN}. &lt;/p&gt;<a name="line.232"></a>
<FONT color="green">233</FONT>         *<a name="line.233"></a>
<FONT color="green">234</FONT>         * @param i {@code i}th regression parameter.<a name="line.234"></a>
<FONT color="green">235</FONT>         * @param j {@code j}th regression parameter.<a name="line.235"></a>
<FONT color="green">236</FONT>         * @return the covariance of the parameter estimates.<a name="line.236"></a>
<FONT color="green">237</FONT>         * @throws OutOfRangeException if {@code i} or {@code j} is not in the<a name="line.237"></a>
<FONT color="green">238</FONT>         * interval {@code [0, number of parameters)}.<a name="line.238"></a>
<FONT color="green">239</FONT>         */<a name="line.239"></a>
<FONT color="green">240</FONT>        public double getCovarianceOfParameters(int i, int j) throws OutOfRangeException {<a name="line.240"></a>
<FONT color="green">241</FONT>            if (parameters == null) {<a name="line.241"></a>
<FONT color="green">242</FONT>                return Double.NaN;<a name="line.242"></a>
<FONT color="green">243</FONT>            }<a name="line.243"></a>
<FONT color="green">244</FONT>            if (i &lt; 0 || i &gt;= this.parameters.length) {<a name="line.244"></a>
<FONT color="green">245</FONT>                throw new OutOfRangeException(i, 0, this.parameters.length - 1);<a name="line.245"></a>
<FONT color="green">246</FONT>            }<a name="line.246"></a>
<FONT color="green">247</FONT>            if (j &lt; 0 || j &gt;= this.parameters.length) {<a name="line.247"></a>
<FONT color="green">248</FONT>                throw new OutOfRangeException(j, 0, this.parameters.length - 1);<a name="line.248"></a>
<FONT color="green">249</FONT>            }<a name="line.249"></a>
<FONT color="green">250</FONT>            return this.getVcvElement(i, j);<a name="line.250"></a>
<FONT color="green">251</FONT>        }<a name="line.251"></a>
<FONT color="green">252</FONT>    <a name="line.252"></a>
<FONT color="green">253</FONT>        /**<a name="line.253"></a>
<FONT color="green">254</FONT>         * &lt;p&gt;Returns the number of parameters estimated in the model.&lt;/p&gt;<a name="line.254"></a>
<FONT color="green">255</FONT>         *<a name="line.255"></a>
<FONT color="green">256</FONT>         * &lt;p&gt;This is the maximum number of regressors, some techniques may drop<a name="line.256"></a>
<FONT color="green">257</FONT>         * redundant parameters&lt;/p&gt;<a name="line.257"></a>
<FONT color="green">258</FONT>         *<a name="line.258"></a>
<FONT color="green">259</FONT>         * @return number of regressors, -1 if not estimated<a name="line.259"></a>
<FONT color="green">260</FONT>         */<a name="line.260"></a>
<FONT color="green">261</FONT>        public int getNumberOfParameters() {<a name="line.261"></a>
<FONT color="green">262</FONT>            if (this.parameters == null) {<a name="line.262"></a>
<FONT color="green">263</FONT>                return -1;<a name="line.263"></a>
<FONT color="green">264</FONT>            }<a name="line.264"></a>
<FONT color="green">265</FONT>            return this.parameters.length;<a name="line.265"></a>
<FONT color="green">266</FONT>        }<a name="line.266"></a>
<FONT color="green">267</FONT>    <a name="line.267"></a>
<FONT color="green">268</FONT>        /**<a name="line.268"></a>
<FONT color="green">269</FONT>         * Returns the number of observations added to the regression model.<a name="line.269"></a>
<FONT color="green">270</FONT>         *<a name="line.270"></a>
<FONT color="green">271</FONT>         * @return Number of observations, -1 if an error condition prevents estimation<a name="line.271"></a>
<FONT color="green">272</FONT>         */<a name="line.272"></a>
<FONT color="green">273</FONT>        public long getN() {<a name="line.273"></a>
<FONT color="green">274</FONT>            return this.nobs;<a name="line.274"></a>
<FONT color="green">275</FONT>        }<a name="line.275"></a>
<FONT color="green">276</FONT>    <a name="line.276"></a>
<FONT color="green">277</FONT>        /**<a name="line.277"></a>
<FONT color="green">278</FONT>         * &lt;p&gt;Returns the sum of squared deviations of the y values about their mean.&lt;/p&gt;<a name="line.278"></a>
<FONT color="green">279</FONT>         *<a name="line.279"></a>
<FONT color="green">280</FONT>         * &lt;p&gt;This is defined as SSTO<a name="line.280"></a>
<FONT color="green">281</FONT>         * &lt;a href="http://www.xycoon.com/SumOfSquares.htm"&gt;here&lt;/a&gt;.&lt;/p&gt;<a name="line.281"></a>
<FONT color="green">282</FONT>         *<a name="line.282"></a>
<FONT color="green">283</FONT>         * &lt;p&gt;If {@code n &lt; 2}, this returns {@code Double.NaN}.&lt;/p&gt;<a name="line.283"></a>
<FONT color="green">284</FONT>         *<a name="line.284"></a>
<FONT color="green">285</FONT>         * @return sum of squared deviations of y values<a name="line.285"></a>
<FONT color="green">286</FONT>         */<a name="line.286"></a>
<FONT color="green">287</FONT>        public double getTotalSumSquares() {<a name="line.287"></a>
<FONT color="green">288</FONT>            return this.globalFitInfo[SST_IDX];<a name="line.288"></a>
<FONT color="green">289</FONT>        }<a name="line.289"></a>
<FONT color="green">290</FONT>    <a name="line.290"></a>
<FONT color="green">291</FONT>        /**<a name="line.291"></a>
<FONT color="green">292</FONT>         * &lt;p&gt;Returns the sum of squared deviations of the predicted y values about<a name="line.292"></a>
<FONT color="green">293</FONT>         * their mean (which equals the mean of y).&lt;/p&gt;<a name="line.293"></a>
<FONT color="green">294</FONT>         *<a name="line.294"></a>
<FONT color="green">295</FONT>         * &lt;p&gt;This is usually abbreviated SSR or SSM.  It is defined as SSM<a name="line.295"></a>
<FONT color="green">296</FONT>         * &lt;a href="http://www.xycoon.com/SumOfSquares.htm"&gt;here&lt;/a&gt;&lt;/p&gt;<a name="line.296"></a>
<FONT color="green">297</FONT>         *<a name="line.297"></a>
<FONT color="green">298</FONT>         * &lt;p&gt;&lt;strong&gt;Preconditions&lt;/strong&gt;: &lt;ul&gt;<a name="line.298"></a>
<FONT color="green">299</FONT>         * &lt;li&gt;At least two observations (with at least two different x values)<a name="line.299"></a>
<FONT color="green">300</FONT>         * must have been added before invoking this method. If this method is<a name="line.300"></a>
<FONT color="green">301</FONT>         * invoked before a model can be estimated, &lt;code&gt;Double.NaN&lt;/code&gt; is<a name="line.301"></a>
<FONT color="green">302</FONT>         * returned.<a name="line.302"></a>
<FONT color="green">303</FONT>         * &lt;/li&gt;&lt;/ul&gt;&lt;/p&gt;<a name="line.303"></a>
<FONT color="green">304</FONT>         *<a name="line.304"></a>
<FONT color="green">305</FONT>         * @return sum of squared deviations of predicted y values<a name="line.305"></a>
<FONT color="green">306</FONT>         */<a name="line.306"></a>
<FONT color="green">307</FONT>        public double getRegressionSumSquares() {<a name="line.307"></a>
<FONT color="green">308</FONT>            return this.globalFitInfo[SST_IDX] - this.globalFitInfo[SSE_IDX];<a name="line.308"></a>
<FONT color="green">309</FONT>        }<a name="line.309"></a>
<FONT color="green">310</FONT>    <a name="line.310"></a>
<FONT color="green">311</FONT>        /**<a name="line.311"></a>
<FONT color="green">312</FONT>         * &lt;p&gt;Returns the &lt;a href="http://www.xycoon.com/SumOfSquares.htm"&gt;<a name="line.312"></a>
<FONT color="green">313</FONT>         * sum of squared errors&lt;/a&gt; (SSE) associated with the regression<a name="line.313"></a>
<FONT color="green">314</FONT>         * model.&lt;/p&gt;<a name="line.314"></a>
<FONT color="green">315</FONT>         *<a name="line.315"></a>
<FONT color="green">316</FONT>         * &lt;p&gt;The return value is constrained to be non-negative - i.e., if due to<a name="line.316"></a>
<FONT color="green">317</FONT>         * rounding errors the computational formula returns a negative result,<a name="line.317"></a>
<FONT color="green">318</FONT>         * 0 is returned.&lt;/p&gt;<a name="line.318"></a>
<FONT color="green">319</FONT>         *<a name="line.319"></a>
<FONT color="green">320</FONT>         * &lt;p&gt;&lt;strong&gt;Preconditions&lt;/strong&gt;: &lt;ul&gt;<a name="line.320"></a>
<FONT color="green">321</FONT>         * &lt;li&gt;numberOfParameters data pairs<a name="line.321"></a>
<FONT color="green">322</FONT>         * must have been added before invoking this method. If this method is<a name="line.322"></a>
<FONT color="green">323</FONT>         * invoked before a model can be estimated, &lt;code&gt;Double,NaN&lt;/code&gt; is<a name="line.323"></a>
<FONT color="green">324</FONT>         * returned.<a name="line.324"></a>
<FONT color="green">325</FONT>         * &lt;/li&gt;&lt;/ul&gt;&lt;/p&gt;<a name="line.325"></a>
<FONT color="green">326</FONT>         *<a name="line.326"></a>
<FONT color="green">327</FONT>         * @return sum of squared errors associated with the regression model<a name="line.327"></a>
<FONT color="green">328</FONT>         */<a name="line.328"></a>
<FONT color="green">329</FONT>        public double getErrorSumSquares() {<a name="line.329"></a>
<FONT color="green">330</FONT>            return this.globalFitInfo[ SSE_IDX];<a name="line.330"></a>
<FONT color="green">331</FONT>        }<a name="line.331"></a>
<FONT color="green">332</FONT>    <a name="line.332"></a>
<FONT color="green">333</FONT>        /**<a name="line.333"></a>
<FONT color="green">334</FONT>         * &lt;p&gt;Returns the sum of squared errors divided by the degrees of freedom,<a name="line.334"></a>
<FONT color="green">335</FONT>         * usually abbreviated MSE.&lt;/p&gt;<a name="line.335"></a>
<FONT color="green">336</FONT>         *<a name="line.336"></a>
<FONT color="green">337</FONT>         * &lt;p&gt;If there are fewer than &lt;strong&gt;numberOfParameters + 1&lt;/strong&gt; data pairs in the model,<a name="line.337"></a>
<FONT color="green">338</FONT>         * or if there is no variation in &lt;code&gt;x&lt;/code&gt;, this returns<a name="line.338"></a>
<FONT color="green">339</FONT>         * &lt;code&gt;Double.NaN&lt;/code&gt;.&lt;/p&gt;<a name="line.339"></a>
<FONT color="green">340</FONT>         *<a name="line.340"></a>
<FONT color="green">341</FONT>         * @return sum of squared deviations of y values<a name="line.341"></a>
<FONT color="green">342</FONT>         */<a name="line.342"></a>
<FONT color="green">343</FONT>        public double getMeanSquareError() {<a name="line.343"></a>
<FONT color="green">344</FONT>            return this.globalFitInfo[ MSE_IDX];<a name="line.344"></a>
<FONT color="green">345</FONT>        }<a name="line.345"></a>
<FONT color="green">346</FONT>    <a name="line.346"></a>
<FONT color="green">347</FONT>        /**<a name="line.347"></a>
<FONT color="green">348</FONT>         * &lt;p&gt;Returns the &lt;a href="http://www.xycoon.com/coefficient1.htm"&gt;<a name="line.348"></a>
<FONT color="green">349</FONT>         * coefficient of multiple determination&lt;/a&gt;,<a name="line.349"></a>
<FONT color="green">350</FONT>         * usually denoted r-square.&lt;/p&gt;<a name="line.350"></a>
<FONT color="green">351</FONT>         *<a name="line.351"></a>
<FONT color="green">352</FONT>         * &lt;p&gt;&lt;strong&gt;Preconditions&lt;/strong&gt;: &lt;ul&gt;<a name="line.352"></a>
<FONT color="green">353</FONT>         * &lt;li&gt;At least numberOfParameters observations (with at least numberOfParameters different x values)<a name="line.353"></a>
<FONT color="green">354</FONT>         * must have been added before invoking this method. If this method is<a name="line.354"></a>
<FONT color="green">355</FONT>         * invoked before a model can be estimated, {@code Double,NaN} is<a name="line.355"></a>
<FONT color="green">356</FONT>         * returned.<a name="line.356"></a>
<FONT color="green">357</FONT>         * &lt;/li&gt;&lt;/ul&gt;&lt;/p&gt;<a name="line.357"></a>
<FONT color="green">358</FONT>         *<a name="line.358"></a>
<FONT color="green">359</FONT>         * @return r-square, a double in the interval [0, 1]<a name="line.359"></a>
<FONT color="green">360</FONT>         */<a name="line.360"></a>
<FONT color="green">361</FONT>        public double getRSquared() {<a name="line.361"></a>
<FONT color="green">362</FONT>            return this.globalFitInfo[ RSQ_IDX];<a name="line.362"></a>
<FONT color="green">363</FONT>        }<a name="line.363"></a>
<FONT color="green">364</FONT>    <a name="line.364"></a>
<FONT color="green">365</FONT>        /**<a name="line.365"></a>
<FONT color="green">366</FONT>         * &lt;p&gt;Returns the adjusted R-squared statistic, defined by the formula &lt;pre&gt;<a name="line.366"></a>
<FONT color="green">367</FONT>         * R&lt;sup&gt;2&lt;/sup&gt;&lt;sub&gt;adj&lt;/sub&gt; = 1 - [SSR (n - 1)] / [SSTO (n - p)]<a name="line.367"></a>
<FONT color="green">368</FONT>         * &lt;/pre&gt;<a name="line.368"></a>
<FONT color="green">369</FONT>         * where SSR is the sum of squared residuals},<a name="line.369"></a>
<FONT color="green">370</FONT>         * SSTO is the total sum of squares}, n is the number<a name="line.370"></a>
<FONT color="green">371</FONT>         * of observations and p is the number of parameters estimated (including the intercept).&lt;/p&gt;<a name="line.371"></a>
<FONT color="green">372</FONT>         *<a name="line.372"></a>
<FONT color="green">373</FONT>         * &lt;p&gt;If the regression is estimated without an intercept term, what is returned is &lt;pre&gt;<a name="line.373"></a>
<FONT color="green">374</FONT>         * &lt;code&gt; 1 - (1 - {@link #getRSquared()} ) * (n / (n - p)) &lt;/code&gt;<a name="line.374"></a>
<FONT color="green">375</FONT>         * &lt;/pre&gt;&lt;/p&gt;<a name="line.375"></a>
<FONT color="green">376</FONT>         *<a name="line.376"></a>
<FONT color="green">377</FONT>         * @return adjusted R-Squared statistic<a name="line.377"></a>
<FONT color="green">378</FONT>         */<a name="line.378"></a>
<FONT color="green">379</FONT>        public double getAdjustedRSquared() {<a name="line.379"></a>
<FONT color="green">380</FONT>            return this.globalFitInfo[ ADJRSQ_IDX];<a name="line.380"></a>
<FONT color="green">381</FONT>        }<a name="line.381"></a>
<FONT color="green">382</FONT>    <a name="line.382"></a>
<FONT color="green">383</FONT>        /**<a name="line.383"></a>
<FONT color="green">384</FONT>         * Returns true if the regression model has been computed including an intercept.<a name="line.384"></a>
<FONT color="green">385</FONT>         * In this case, the coefficient of the intercept is the first element of the<a name="line.385"></a>
<FONT color="green">386</FONT>         * {@link #getParameterEstimates() parameter estimates}.<a name="line.386"></a>
<FONT color="green">387</FONT>         * @return true if the model has an intercept term<a name="line.387"></a>
<FONT color="green">388</FONT>         */<a name="line.388"></a>
<FONT color="green">389</FONT>        public boolean hasIntercept() {<a name="line.389"></a>
<FONT color="green">390</FONT>            return this.containsConstant;<a name="line.390"></a>
<FONT color="green">391</FONT>        }<a name="line.391"></a>
<FONT color="green">392</FONT>    <a name="line.392"></a>
<FONT color="green">393</FONT>        /**<a name="line.393"></a>
<FONT color="green">394</FONT>         * Gets the i-jth element of the variance-covariance matrix.<a name="line.394"></a>
<FONT color="green">395</FONT>         *<a name="line.395"></a>
<FONT color="green">396</FONT>         * @param i first variable index<a name="line.396"></a>
<FONT color="green">397</FONT>         * @param j second variable index<a name="line.397"></a>
<FONT color="green">398</FONT>         * @return the requested variance-covariance matrix entry<a name="line.398"></a>
<FONT color="green">399</FONT>         */<a name="line.399"></a>
<FONT color="green">400</FONT>        private double getVcvElement(int i, int j) {<a name="line.400"></a>
<FONT color="green">401</FONT>            if (this.isSymmetricVCD) {<a name="line.401"></a>
<FONT color="green">402</FONT>                if (this.varCovData.length &gt; 1) {<a name="line.402"></a>
<FONT color="green">403</FONT>                    //could be stored in upper or lower triangular<a name="line.403"></a>
<FONT color="green">404</FONT>                    if (i == j) {<a name="line.404"></a>
<FONT color="green">405</FONT>                        return varCovData[i][i];<a name="line.405"></a>
<FONT color="green">406</FONT>                    } else if (i &gt;= varCovData[j].length) {<a name="line.406"></a>
<FONT color="green">407</FONT>                        return varCovData[i][j];<a name="line.407"></a>
<FONT color="green">408</FONT>                    } else {<a name="line.408"></a>
<FONT color="green">409</FONT>                        return varCovData[j][i];<a name="line.409"></a>
<FONT color="green">410</FONT>                    }<a name="line.410"></a>
<FONT color="green">411</FONT>                } else {//could be in single array<a name="line.411"></a>
<FONT color="green">412</FONT>                    if (i &gt; j) {<a name="line.412"></a>
<FONT color="green">413</FONT>                        return varCovData[0][(i + 1) * i / 2 + j];<a name="line.413"></a>
<FONT color="green">414</FONT>                    } else {<a name="line.414"></a>
<FONT color="green">415</FONT>                        return varCovData[0][(j + 1) * j / 2 + i];<a name="line.415"></a>
<FONT color="green">416</FONT>                    }<a name="line.416"></a>
<FONT color="green">417</FONT>                }<a name="line.417"></a>
<FONT color="green">418</FONT>            } else {<a name="line.418"></a>
<FONT color="green">419</FONT>                return this.varCovData[i][j];<a name="line.419"></a>
<FONT color="green">420</FONT>            }<a name="line.420"></a>
<FONT color="green">421</FONT>        }<a name="line.421"></a>
<FONT color="green">422</FONT>    }<a name="line.422"></a>




























































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